ANALYSIS OF KERNEL FUNCTION VARIATIONS ON AGRICULTURAL INDUSTRY SECTOR STOCK PREDICTION USING SUPPORT VECTOR REGRESSION METHOD

• Main Author: Rahmah, Gesti
• Format: Final Project
• Language: Indonesia
• Subjects: Fisika
• Online Access: https://digilib.itb.ac.id/gdl/view/46477
• Institution: Institut Teknologi Bandung

Agriculture is a sector that has a significant role for the Indonesian economy. According to data from the Central Statistics Agency, in 2012, the agricultural sector absorbed 35.9% of the total workforce in Indonesia and contributed 14.7% to Indonesia's GNP. Whereas in the second quarter of 2018 the contribution of agriculture to the growth rate of gross domestic product (GDP) reached 13.63%. The agriculture sector is very promising to be a business and investment goal. The potential of extraordinary natural resources, the number of requests that are numerous, continuously increasing and sustainable is a promising business opportunity for investors. Based on these conditions, it is necessary to have a model to predict the condition of the stock price index in the agricultural industry sector to assist investors in making decisions in investment or stocks. In this final project, Support Vector Regression (SVR) method is used to forecast the closing stock price index in the agricultural sector industry. SVR is a forecasting method by obtaining an optimal separator function to separate two data sets from two different classes. Preprocessing data will be performed before its used as data in the SVR learning process. Preprocessing data divides data into two parts, training data and test data. The training data is used for the learning process of the SVR method so as to produce an optimal separating function. After obtaining the prediction results for the stock price index, an analysis of accuracy will be performed by calculating the error value and comparing the effect of variations of the Kernel function on the accuracy of the prediction.